{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 简答题",
   "id": "5e54f3a96c9ec673"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "1. Glorot初始化和He初始化为了解决什么问题\n",
    "解决了梯度爆炸和梯度消失的问题\n",
    "2. 是否可以将所有权重初始化为相同的值（只要该值是使用He初始化随机选择的）\n",
    "不可以，因为相同的值会导致同一层的神经元在反向传播时获得相同的梯度更新，导致神经元学习相同的特征\n",
    "而且神经网络需要多样性，相同的话会导致表达能力受限\n",
    "3. 将偏置项初始化为0可以吗\n",
    "大多数情况是可以的\n",
    "4. 总结下讨论过的激活函数，并讲述在什么情况下使用它们\n",
    "sigmoid,在二分类输出层使用他\n",
    "relu，在最常用的隐藏层\n",
    "\n",
    "5. 如果在使用SGD优化器时将momentum超参数设置得太接近1（例如0.99999)会发生什么情况\n",
    "过度平滑，收敛变慢，可能发散，降低适应度\n",
    "6. 列举三种能产生稀疏模型得方法\n",
    "l1正则化，迭代权重剪枝，Dropout+后处理剪枝\n",
    "7. dropout会减慢训练速度吗？它会减慢推理（即对新实例进行预测）速度吗？MC dropout呢？\n",
    "会减慢训练速度，但不会减慢推理速度，后者训练速度相同，推理速度显著减慢"
   ],
   "id": "de34621cb987cb5"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 编程题",
   "id": "bd377a821663e9d"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "在CIFAR10图像数据集上练习训练深度神经网络：\n",
    "\n",
    "CIFAR-10数据集，又称加拿大高等研究院数据集（Canadian Institute for Advanced Research）是一个常用于训练机器学习和计算机视觉算法的图像集合。它是最广泛使用的机器学习研究数据集之一。\n",
    "\n",
    "CIFAR-10数据集包含60,000张32×32像素的彩色图像，分为10个不同的类别。这10个类别分别是飞机、汽车、鸟类、猫、鹿、狗、青蛙、马、船和卡车，每个类别有6,000张图片。\n",
    "\n",
    "飞机、汽车、鸟类、猫、鹿、狗、青蛙、马、船和卡车 对应的分类编码是0，1，2，3，4，5，6，7，8，9\n",
    "\n",
    "\n",
    "1. 构建一个DNN，使其包含20个隐藏层，每个隐藏层包含100个神经元。使用He初始化和Swish激活函数。\n",
    "2. 使用Nadam优化和早停技术，在CIFAR10数据集上训练网络。可以使用tf.keras.datasets.cifar10.load_data()加载数据。该数据集由10个类别的60000幅32×32像素的彩色图像（用于训练的50000个，用于测试的10000个）组成，因此需要一个具有10个神经元的softmax输出层。记住，每次更改模型的架构或超参数时，都要找寻正确的学习率。\n",
    "3. 尝试添加批量归一化并比较学习曲线：收敛速度是否比以前快？会产生更好的模型吗？它如何影响训练速度？\n",
    "4. 尝试用SELU替换批量归一化，并进行必要的调整以确保网络是自归一化的（即归一化输入特征，使用LeCun正态初始化，确保DNN仅仅包含一系列的密集层等）\n",
    "5. 尝试使用Alpha dropout正则化模型。然后，在不重新训练模型的情况下，看看是否可以使用MC dropout获得更好的精度。\n",
    "6. 使用1周期调度来重新训练模型，看看它是否可以提高训练速度和模型精度。"
   ],
   "id": "64fa4c035dd6baa6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "加载数据的代码： tf.keras.datasets.cifar10.load_data()\n",
    "\n",
    "下载失败的解决方案：\n",
    "1. 下载文件： cifar-10-python.tar.gz （会把文件发群里）\n",
    "2. 将文件 cifar-10-python.tar.gz 重命名为 cifar-10-batches-py.tar.gz\n",
    "3. 并复制到类似 C:\\Users\\某个用户名或者管理员\\.keras\\datasets 的路径。将 某个用户名或者管理员 替换为你的用户名。如果是 Linux/Macos 系统，则应为 /home/某个用户名或者管理员/.keras/datasets。"
   ],
   "id": "a55e1957ace4b324"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T08:37:43.580184Z",
     "start_time": "2025-09-08T08:36:20.313081Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 加载数据的代码\n",
    "import tensorflow as tf\n",
    "(X_train_full, y_train_full), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "# 查看数据集形状\n",
    "print(\"训练集图片形状:\", X_train_full.shape) # 应该是 (50000, 32, 32, 3)\n",
    "print(\"训练集标签形状:\", y_train_full.shape) # 应该是 (50000, 1)"
   ],
   "id": "72a6c219e04bafc7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "\u001B[1m170498071/170498071\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m53s\u001B[0m 0us/step\n",
      "训练集图片形状: (50000, 32, 32, 3)\n",
      "训练集标签形状: (50000, 1)\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T08:37:55.510758Z",
     "start_time": "2025-09-08T08:37:55.501783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 按0-9顺序定义类别：飞机、汽车、鸟类、猫、鹿、狗、青蛙、马、船和卡车\n",
    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "               'dog', 'frog', 'horse', 'ship', 'truck']"
   ],
   "id": "f620dcb2976c1abe",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T08:37:59.658992Z",
     "start_time": "2025-09-08T08:37:57.658123Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nrows = 5\n",
    "ncols = 5\n",
    "\n",
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "\n",
    "random_indices = np.random.choice(len(X_train_full), nrows * ncols, replace=False)  # 无放回随机抽索引\n",
    "\n",
    "for i, idx in enumerate(random_indices):\n",
    "    plt.subplot(nrows, ncols, i+1)\n",
    "    img = X_train_full[idx]\n",
    "    label = y_train_full[idx, 0]\n",
    "    plt.imshow(img)\n",
    "    plt.title(class_names[label])\n",
    "    plt.axis('off')\n",
    "\n",
    "# 调整子图之间的间距，防止标题等重叠\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "e44a7460f0e7348c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 500x500 with 25 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-08T10:08:48.114846Z",
     "start_time": "2025-09-08T10:06:07.014818Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from functools import partial\n",
    "\n",
    "# 设置随机种子以确保可重复性\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# 1. 加载和预处理数据\n",
    "print(\"加载CIFAR-10数据集...\")\n",
    "(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "\n",
    "# 分割训练集和验证集\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_full, y_train_full, test_size=0.1, random_state=42\n",
    ")\n",
    "\n",
    "# 数据预处理\n",
    "def preprocess_images(images):\n",
    "    \"\"\"将图像数据预处理为适合DNN的格式\"\"\"\n",
    "    images = images.astype('float32') / 255.0  # 归一化到[0,1]\n",
    "    images = images.reshape(images.shape[0], -1)  # 展平为1D向量\n",
    "    return images\n",
    "\n",
    "X_train = preprocess_images(X_train)\n",
    "X_val = preprocess_images(X_val)\n",
    "X_test = preprocess_images(X_test)\n",
    "\n",
    "# 标准化特征\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_val = scaler.transform(X_val)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# 2. 构建基础DNN模型（20个隐藏层，每层100个神经元）\n",
    "print(\"构建深度神经网络...\")\n",
    "\n",
    "# 使用He初始化和Swish激活函数\n",
    "HeNormal = keras.initializers.HeNormal(seed=42)\n",
    "RegularDense = partial(keras.layers.Dense,\n",
    "                      units=100,\n",
    "                      kernel_initializer=HeNormal,\n",
    "                      activation=\"swish\")\n",
    "\n",
    "def build_dnn_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(X_train.shape[1],)))\n",
    "\n",
    "    # 添加20个隐藏层\n",
    "    for _ in range(20):\n",
    "        model.add(RegularDense())\n",
    "\n",
    "    # 输出层\n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    return model\n",
    "\n",
    "# 3. 训练基础模型\n",
    "print(\"训练基础DNN模型...\")\n",
    "\n",
    "model = build_dnn_model()\n",
    "\n",
    "# 使用Nadam优化器\n",
    "optimizer = keras.optimizers.Nadam(learning_rate=1e-4)\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss=\"sparse_categorical_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "# 早停回调\n",
    "early_stopping = keras.callbacks.EarlyStopping(\n",
    "    patience=10, restore_best_weights=True\n",
    ")\n",
    "\n",
    "# 学习率调度器\n",
    "lr_scheduler = keras.callbacks.ReduceLROnPlateau(\n",
    "    factor=0.5, patience=5\n",
    ")\n",
    "\n",
    "history = model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=100,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=128,\n",
    "    callbacks=[early_stopping, lr_scheduler],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# 评估基础模型\n",
    "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"\\n基础模型测试准确率: {test_acc:.4f}\")\n",
    "\n",
    "# 4. 添加批量归一化的模型\n",
    "print(\"\\n训练带批量归一化的DNN模型...\")\n",
    "\n",
    "def build_bn_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(X_train.shape[1],)))\n",
    "\n",
    "    for _ in range(20):\n",
    "        model.add(RegularDense())\n",
    "        model.add(keras.layers.BatchNormalization())\n",
    "\n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    return model\n",
    "\n",
    "bn_model = build_bn_model()\n",
    "bn_model.compile(optimizer=optimizer,\n",
    "                loss=\"sparse_categorical_crossentropy\",\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "bn_history = bn_model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=100,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=128,\n",
    "    callbacks=[early_stopping, lr_scheduler],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "bn_test_loss, bn_test_acc = bn_model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"批量归一化模型测试准确率: {bn_test_acc:.4f}\")\n",
    "\n",
    "# 5. 使用SELU激活函数的自归一化网络\n",
    "print(\"\\n训练SELU自归一化网络...\")\n",
    "\n",
    "def build_selu_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(X_train.shape[1],)))\n",
    "\n",
    "    # 使用LeCun正态初始化\n",
    "    for _ in range(20):\n",
    "        model.add(keras.layers.Dense(\n",
    "            100,\n",
    "            kernel_initializer=\"lecun_normal\",\n",
    "            activation=\"selu\"\n",
    "        ))\n",
    "\n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    return model\n",
    "\n",
    "selu_model = build_selu_model()\n",
    "selu_model.compile(optimizer=optimizer,\n",
    "                  loss=\"sparse_categorical_crossentropy\",\n",
    "                  metrics=[\"accuracy\"])\n",
    "\n",
    "selu_history = selu_model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=100,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=128,\n",
    "    callbacks=[early_stopping, lr_scheduler],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "selu_test_loss, selu_test_acc = selu_model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"SELU模型测试准确率: {selu_test_acc:.4f}\")\n",
    "\n",
    "# 6. Alpha Dropout正则化\n",
    "print(\"\\n训练带Alpha Dropout的模型...\")\n",
    "\n",
    "def build_alpha_dropout_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(X_train.shape[1],)))\n",
    "\n",
    "    for _ in range(20):\n",
    "        model.add(keras.layers.Dense(\n",
    "            100,\n",
    "            kernel_initializer=\"lecun_normal\",\n",
    "            activation=\"selu\"\n",
    "        ))\n",
    "        model.add(keras.layers.AlphaDropout(rate=0.1))\n",
    "\n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    return model\n",
    "\n",
    "alpha_model = build_alpha_dropout_model()\n",
    "alpha_model.compile(optimizer=optimizer,\n",
    "                   loss=\"sparse_categorical_crossentropy\",\n",
    "                   metrics=[\"accuracy\"])\n",
    "\n",
    "alpha_history = alpha_model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=100,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=128,\n",
    "    callbacks=[early_stopping, lr_scheduler],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "alpha_test_loss, alpha_test_acc = alpha_model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"Alpha Dropout模型测试准确率: {alpha_test_acc:.4f}\")\n",
    "\n",
    "# 7. MC Dropout评估\n",
    "print(\"\\n使用MC Dropout进行评估...\")\n",
    "\n",
    "def mc_dropout_predict(model, X, n_samples=50):\n",
    "    \"\"\"MC Dropout预测\"\"\"\n",
    "    # 启用训练模式以保持dropout\n",
    "    y_probas = []\n",
    "    for _ in range(n_samples):\n",
    "        y_proba = model(X, training=True)\n",
    "        y_probas.append(y_proba)\n",
    "\n",
    "    return np.mean(y_probas, axis=0)\n",
    "\n",
    "# 对测试集进行MC Dropout预测\n",
    "y_probas_mc = mc_dropout_predict(alpha_model, X_test)\n",
    "y_pred_mc = np.argmax(y_probas_mc, axis=1)\n",
    "mc_accuracy = np.mean(y_pred_mc == y_test.flatten())\n",
    "print(f\"MC Dropout测试准确率: {mc_accuracy:.4f}\")\n",
    "\n",
    "# 8. 1周期调度训练\n",
    "print(\"\\n使用1周期调度训练模型...\")\n",
    "\n",
    "def build_final_model():\n",
    "    model = keras.Sequential()\n",
    "    model.add(keras.layers.Input(shape=(X_train.shape[1],)))\n",
    "\n",
    "    for _ in range(20):\n",
    "        model.add(keras.layers.Dense(\n",
    "            100,\n",
    "            kernel_initializer=\"lecun_normal\",\n",
    "            activation=\"selu\"\n",
    "        ))\n",
    "        model.add(keras.layers.BatchNormalization())\n",
    "        model.add(keras.layers.AlphaDropout(rate=0.1))\n",
    "\n",
    "    model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
    "    return model\n",
    "\n",
    "final_model = build_final_model()\n",
    "\n",
    "# 1周期调度\n",
    "initial_learning_rate = 1e-4\n",
    "max_learning_rate = 1e-2\n",
    "final_learning_rate = 1e-6\n",
    "epochs = 100\n",
    "steps_per_epoch = len(X_train) // 128\n",
    "\n",
    "lr_schedule = keras.optimizers.schedules.CyclicalLearningRate(\n",
    "    initial_learning_rate=initial_learning_rate,\n",
    "    maximal_learning_rate=max_learning_rate,\n",
    "    step_size=2 * steps_per_epoch,\n",
    "    scale_fn=lambda x: 1/(2.**(x-1)),\n",
    "    scale_mode='cycle'\n",
    ")\n",
    "\n",
    "optimizer = keras.optimizers.Nadam(learning_rate=lr_schedule)\n",
    "final_model.compile(optimizer=optimizer,\n",
    "                   loss=\"sparse_categorical_crossentropy\",\n",
    "                   metrics=[\"accuracy\"])\n",
    "\n",
    "final_history = final_model.fit(\n",
    "    X_train, y_train,\n",
    "    epochs=epochs,\n",
    "    validation_data=(X_val, y_val),\n",
    "    batch_size=128,\n",
    "    callbacks=[early_stopping],\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "final_test_loss, final_test_acc = final_model.evaluate(X_test, y_test, verbose=0)\n",
    "print(f\"最终模型测试准确率: {final_test_acc:.4f}\")\n",
    "\n",
    "# 绘制学习曲线\n",
    "def plot_learning_curves(histories, labels):\n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    for i, history in enumerate(histories):\n",
    "        plt.plot(history.history['val_accuracy'], label=labels[i])\n",
    "\n",
    "    plt.title('模型验证准确率比较')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Validation Accuracy')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# 比较所有模型\n",
    "histories = [history, bn_history, selu_history, alpha_history, final_history]\n",
    "labels = ['基础DNN', '批量归一化', 'SELU', 'Alpha Dropout', '最终模型']\n",
    "plot_learning_curves(histories, labels)\n",
    "\n",
    "# 结果总结\n",
    "print(\"\\n=== 模型性能总结 ===\")\n",
    "print(f\"基础DNN: {test_acc:.4f}\")\n",
    "print(f\"批量归一化: {bn_test_acc:.4f}\")\n",
    "print(f\"SELU: {selu_test_acc:.4f}\")\n",
    "print(f\"Alpha Dropout: {alpha_test_acc:.4f}\")\n",
    "print(f\"MC Dropout: {mc_accuracy:.4f}\")\n",
    "print(f\"最终模型: {final_test_acc:.4f}\")"
   ],
   "id": "5dd11593383cea56",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载CIFAR-10数据集...\n",
      "构建深度神经网络...\n",
      "训练基础DNN模型...\n",
      "Epoch 1/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m17s\u001B[0m 19ms/step - accuracy: 0.1698 - loss: 2.1579 - val_accuracy: 0.2710 - val_loss: 1.9053 - learning_rate: 1.0000e-04\n",
      "Epoch 2/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 21ms/step - accuracy: 0.3194 - loss: 1.8059 - val_accuracy: 0.3290 - val_loss: 1.7755 - learning_rate: 1.0000e-04\n",
      "Epoch 3/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 18ms/step - accuracy: 0.3778 - loss: 1.6676 - val_accuracy: 0.3654 - val_loss: 1.7032 - learning_rate: 1.0000e-04\n",
      "Epoch 4/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 18ms/step - accuracy: 0.4242 - loss: 1.5680 - val_accuracy: 0.3996 - val_loss: 1.6592 - learning_rate: 1.0000e-04\n",
      "Epoch 5/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 21ms/step - accuracy: 0.4569 - loss: 1.4845 - val_accuracy: 0.4160 - val_loss: 1.6392 - learning_rate: 1.0000e-04\n",
      "Epoch 6/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m9s\u001B[0m 18ms/step - accuracy: 0.4846 - loss: 1.4133 - val_accuracy: 0.4298 - val_loss: 1.6328 - learning_rate: 1.0000e-04\n",
      "Epoch 7/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 18ms/step - accuracy: 0.5050 - loss: 1.3538 - val_accuracy: 0.4372 - val_loss: 1.6316 - learning_rate: 1.0000e-04\n",
      "Epoch 8/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 20ms/step - accuracy: 0.5250 - loss: 1.3007 - val_accuracy: 0.4364 - val_loss: 1.6484 - learning_rate: 1.0000e-04\n",
      "Epoch 9/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 17ms/step - accuracy: 0.5407 - loss: 1.2540 - val_accuracy: 0.4360 - val_loss: 1.6643 - learning_rate: 1.0000e-04\n",
      "Epoch 10/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 20ms/step - accuracy: 0.5579 - loss: 1.2119 - val_accuracy: 0.4386 - val_loss: 1.6904 - learning_rate: 1.0000e-04\n",
      "Epoch 11/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m9s\u001B[0m 17ms/step - accuracy: 0.5693 - loss: 1.1749 - val_accuracy: 0.4342 - val_loss: 1.7266 - learning_rate: 1.0000e-04\n",
      "Epoch 12/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 20ms/step - accuracy: 0.5818 - loss: 1.1413 - val_accuracy: 0.4364 - val_loss: 1.7623 - learning_rate: 1.0000e-04\n",
      "Epoch 13/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 18ms/step - accuracy: 0.5930 - loss: 1.1102 - val_accuracy: 0.4420 - val_loss: 1.7723 - learning_rate: 5.0000e-05\n",
      "Epoch 14/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 18ms/step - accuracy: 0.6068 - loss: 1.0710 - val_accuracy: 0.4440 - val_loss: 1.8085 - learning_rate: 5.0000e-05\n",
      "Epoch 15/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m10s\u001B[0m 17ms/step - accuracy: 0.6186 - loss: 1.0420 - val_accuracy: 0.4436 - val_loss: 1.8486 - learning_rate: 5.0000e-05\n",
      "Epoch 16/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m7s\u001B[0m 19ms/step - accuracy: 0.6285 - loss: 1.0152 - val_accuracy: 0.4422 - val_loss: 1.8927 - learning_rate: 5.0000e-05\n",
      "Epoch 17/100\n",
      "\u001B[1m352/352\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 17ms/step - accuracy: 0.6377 - loss: 0.9911 - val_accuracy: 0.4398 - val_loss: 1.9470 - learning_rate: 5.0000e-05\n",
      "\n",
      "基础模型测试准确率: 0.4489\n",
      "\n",
      "训练带批量归一化的DNN模型...\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "ename": "NotImplementedError",
     "evalue": "numpy() is only available when eager execution is enabled.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[4], line 114\u001B[0m\n\u001B[0;32m    109\u001B[0m bn_model \u001B[38;5;241m=\u001B[39m build_bn_model()\n\u001B[0;32m    110\u001B[0m bn_model\u001B[38;5;241m.\u001B[39mcompile(optimizer\u001B[38;5;241m=\u001B[39moptimizer,\n\u001B[0;32m    111\u001B[0m                 loss\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msparse_categorical_crossentropy\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    112\u001B[0m                 metrics\u001B[38;5;241m=\u001B[39m[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124maccuracy\u001B[39m\u001B[38;5;124m\"\u001B[39m])\n\u001B[1;32m--> 114\u001B[0m bn_history \u001B[38;5;241m=\u001B[39m bn_model\u001B[38;5;241m.\u001B[39mfit(\n\u001B[0;32m    115\u001B[0m     X_train, y_train,\n\u001B[0;32m    116\u001B[0m     epochs\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m,\n\u001B[0;32m    117\u001B[0m     validation_data\u001B[38;5;241m=\u001B[39m(X_val, y_val),\n\u001B[0;32m    118\u001B[0m     batch_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m128\u001B[39m,\n\u001B[0;32m    119\u001B[0m     callbacks\u001B[38;5;241m=\u001B[39m[early_stopping, lr_scheduler],\n\u001B[0;32m    120\u001B[0m     verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m\n\u001B[0;32m    121\u001B[0m )\n\u001B[0;32m    123\u001B[0m bn_test_loss, bn_test_acc \u001B[38;5;241m=\u001B[39m bn_model\u001B[38;5;241m.\u001B[39mevaluate(X_test, y_test, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\n\u001B[0;32m    124\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m批量归一化模型测试准确率: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mbn_test_acc\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m.4f\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[1;32mD:\\anaconda\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:122\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    119\u001B[0m     filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[0;32m    120\u001B[0m     \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[0;32m    121\u001B[0m     \u001B[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001B[39;00m\n\u001B[1;32m--> 122\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    123\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[0;32m    124\u001B[0m     \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n",
      "File \u001B[1;32mD:\\anaconda\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\core.py:171\u001B[0m, in \u001B[0;36mconvert_to_numpy\u001B[1;34m(x)\u001B[0m\n\u001B[0;32m    169\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(x, tf\u001B[38;5;241m.\u001B[39mRaggedTensor):\n\u001B[0;32m    170\u001B[0m     x \u001B[38;5;241m=\u001B[39m x\u001B[38;5;241m.\u001B[39mto_tensor()\n\u001B[1;32m--> 171\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m np\u001B[38;5;241m.\u001B[39marray(x)\n",
      "\u001B[1;31mNotImplementedError\u001B[0m: numpy() is only available when eager execution is enabled."
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "afad774bbf64e02c"
  }
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